{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "    print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ 33.51648769   9.01472056   1.937846  ]\n",
      "  stds:  [ 30.90530094  30.87378906  26.11112466]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [ -7.93809463e-17   1.41917728e-16  -1.38777878e-17]\n",
      "  std:  [ 0.99999999  0.99999999  0.99999999]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999998]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print 'Before batch normalization:'\n",
    "print '  means: ', a.mean(axis=0)\n",
    "print '  stds: ', a.std(axis=0)\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print 'After batch normalization (gamma=1, beta=0)'\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print '  mean: ', a_norm.mean(axis=0)\n",
    "print '  std: ', a_norm.std(axis=0)\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print 'After batch normalization (nontrivial gamma, beta)'\n",
    "print '  means: ', a_norm.mean(axis=0)\n",
    "print '  stds: ', a_norm.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.08881538  0.03341424  0.05475931]\n",
      "  stds:  [ 0.96406482  0.9809237   0.91320516]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in xrange(50):\n",
    "    X = np.random.randn(N, D1)\n",
    "    a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "    batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print 'After batch normalization (test-time):'\n",
    "print '  means: ', a_norm.mean(axis=0)\n",
    "print '  stds: ', a_norm.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  7.00248589978e-10\n",
      "dgamma error:  5.53901393168e-11\n",
      "dbeta error:  2.27568703612e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dgamma error: ', rel_error(da_num, dgamma)\n",
    "print 'dbeta error: ', rel_error(db_num, dbeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: You can still complete the rest of the assignment if you don't figure this part out, so don't worry too much if you can't get it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  0.0\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.10x\n"
     ]
    }
   ],
   "source": [
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print 'dx difference: ', rel_error(dx1, dx2)\n",
    "print 'dgamma difference: ', rel_error(dgamma1, dgamma2)\n",
    "print 'dbeta difference: ', rel_error(dbeta1, dbeta2)\n",
    "print 'speedup: %.2fx' % ((t2 - t1) / (t3 - t2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2124121956\n",
      "W1 relative error: 7.86e-06\n",
      "W2 relative error: 2.86e-05\n",
      "W3 relative error: 1.73e-09\n",
      "b1 relative error: 2.22e-03\n",
      "b2 relative error: 2.66e-07\n",
      "b3 relative error: 1.39e-10\n",
      "beta1 relative error: 4.72e-09\n",
      "beta2 relative error: 4.15e-09\n",
      "gamma1 relative error: 4.69e-09\n",
      "gamma2 relative error: 4.57e-08\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.91857008332\n",
      "W1 relative error: 4.29e-05\n",
      "W2 relative error: 1.38e-06\n",
      "W3 relative error: 1.08e-08\n",
      "b1 relative error: 2.85e-07\n",
      "b2 relative error: 2.08e-07\n",
      "b3 relative error: 2.88e-10\n",
      "beta1 relative error: 3.75e-09\n",
      "beta2 relative error: 7.77e-09\n",
      "gamma1 relative error: 3.82e-09\n",
      "gamma2 relative error: 7.80e-09\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "    print 'Running check with reg = ', reg\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print 'Initial loss: ', loss\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))\n",
    "    if reg == 0: print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.264534\n",
      "(Epoch 0 / 10) train acc: 0.123000; val_acc: 0.148000\n",
      "(Epoch 1 / 10) train acc: 0.336000; val_acc: 0.251000\n",
      "(Epoch 2 / 10) train acc: 0.411000; val_acc: 0.312000\n",
      "(Epoch 3 / 10) train acc: 0.468000; val_acc: 0.320000\n",
      "(Epoch 4 / 10) train acc: 0.524000; val_acc: 0.318000\n",
      "(Epoch 5 / 10) train acc: 0.597000; val_acc: 0.320000\n",
      "(Epoch 6 / 10) train acc: 0.626000; val_acc: 0.318000\n",
      "(Epoch 7 / 10) train acc: 0.702000; val_acc: 0.361000\n",
      "(Epoch 8 / 10) train acc: 0.738000; val_acc: 0.329000\n",
      "(Epoch 9 / 10) train acc: 0.787000; val_acc: 0.309000\n",
      "(Epoch 10 / 10) train acc: 0.794000; val_acc: 0.297000\n",
      "(Iteration 1 / 200) loss: 2.303145\n",
      "(Epoch 0 / 10) train acc: 0.106000; val_acc: 0.122000\n",
      "(Epoch 1 / 10) train acc: 0.231000; val_acc: 0.210000\n",
      "(Epoch 2 / 10) train acc: 0.280000; val_acc: 0.230000\n",
      "(Epoch 3 / 10) train acc: 0.326000; val_acc: 0.294000\n",
      "(Epoch 4 / 10) train acc: 0.358000; val_acc: 0.273000\n",
      "(Epoch 5 / 10) train acc: 0.387000; val_acc: 0.285000\n",
      "(Epoch 6 / 10) train acc: 0.440000; val_acc: 0.296000\n",
      "(Epoch 7 / 10) train acc: 0.456000; val_acc: 0.285000\n",
      "(Epoch 8 / 10) train acc: 0.556000; val_acc: 0.309000\n",
      "(Epoch 9 / 10) train acc: 0.611000; val_acc: 0.296000\n",
      "(Epoch 10 / 10) train acc: 0.632000; val_acc: 0.315000\n"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3QAAANsCAYAAAATFepNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10593d8ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20"
     ]
    }
   ],
   "source": [
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print 'Running weight scale %d / %d' % (i + 1, len(weight_scales))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAATeCAYAAABANywRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRNLo/wFB7x8EPtpzFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc5562f150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "    best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "    bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "\n",
    "    best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "    bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "\n",
    "    final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "    bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer:\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
